DVC vs Git LFS

Side-by-side — pricing, strengths, tradeoffs, and fit.

DVC

Open-source version control system for machine learning projects with pipelines support.

Git LFS

Version large files with Git using an open-source extension.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
· Not enough data·From $0

Overview

One-liner

Open-source version control system for machine learning projects with pipelines support.

One-liner

Version large files with Git using an open-source extension.

Maturity

Established

Maturity

Not enough data

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Teams working with large datasets that need version control beyond Git's capabilities
Projects requiring reproducible experiments and pipelines across multiple developers
Data science teams looking to integrate ML workflows into their existing Git-based projects

✓ Best for

Teams that need to version control large files without bloating their Git repositories.
Projects involving large datasets or media assets where file size management is critical.
Developers who require a seamless integration with existing Git workflows for managing large files.

Not ideal for

✕ Not for

Real-time data processing systems where immediate changes are critical
Teams preferring a cloud-managed solution for version control of machine learning artifacts

✕ Not for

Scenarios requiring real-time collaboration on large files, as Git LFS does not support this feature.
Users looking for cloud-hosted solutions without the need to manage their own servers or storage.

Strengths

↑ Strengths

Version control for machine learning projects
Reproducibility of experiments and pipelines
Integration with Git for versioning
Support for large data files through remote storage integration

↑ Strengths

Version control for large files without bloating repositories.
Stores file contents on a remote server to keep the repository size manageable.
Integrates seamlessly with Git workflows.
Supports various file types including media, data sets, and binaries.

Weaknesses

↓ Weaknesses

Limited integrations with non-Python data science tools
Performance issues with large datasets

↓ Weaknesses

Complex setup for integrating with existing Git workflows
Performance overhead when handling large files
Limited language support beyond the primary Go implementation
Dependency on a remote server for storing file contents
Full DVC profile →Full Git LFS profile →